Details

Autor(en) / Beteiligte

• Kong, Xiangjie
• Xia, Feng
• Ning, Zhaolong
• Rahim, Azizur
• Cai, Yinqiong
• Gao, Zhiqiang
• Ma, Jianhua

Titel

Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data

Ist Teil von

• IEEE transactions on vehicular technology, 2018-05, Vol.67 (5), p.3874-3886

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2018

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin-destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization.